Systems, methods, and computer program products for estimating crowd sizes using information collected from mobile devices in a wireless communications network

ABSTRACT

Systems, methods, and computer program products are for estimating crowd size at a location. An exemplary method includes determining, at a crowd size analyzer, a number of wireless service users at the location, and estimating, at the crowd size analyzer, a total number of people at the location based upon the number of wireless service users determined to be at the location.

This application is a continuation of U.S. patent application Ser. No.12/791,463, filed Jun. 1, 2010, now U.S. Pat. No. 8,442,807.

TECHNICAL FIELD

The present disclosure relates generally to crowd size estimation and,more particularly, to systems, methods, and computer program productsfor estimating crowd sizes using information collected from mobiledevices in a wireless communications network.

BACKGROUND

Crowd size estimation is an important issue for event organizers, lawenforcement, urban planners, and news media, among others, who wantaccurate estimates of crowd size at a specific time and location. Forexample, organizers may want accurate estimates of turnout todemonstrate support for their cause; law enforcement, which needs tooptimally allocate critical resources to secure an event, may wantaccurate crowd size estimates to ensure that resources are allocated inthe best and most cost effective manner; urban planners may use crowdssize estimates to design improved solutions for handling crowds inpublic spaces such as parks, sports venues, and the like; and news mediamay use crowd size estimates to report the attendance at an event.

Several methods for crowd estimation have been proposed with varyingdegrees of success. One such method uses aerial photographic analysis toestimate crowd sizes In the aerial photographic analysis method, afixed-wing aircraft is used to take aerial photographs at an altitude of2,000 feet or less. Photographs of the area are taken in strips using adigital camera with about sixty-percent overlap between successivepictures to allow stereoscopic viewing. An image resolution of about onefoot per pixel is typically used. The photographs are then loaded in animage processing program and co-registered with a one-meter-resolutionUnited States Geological Survey ortho-photo map—a perspective-correctedcollage of aerial shots of the area with a uniform scale. A grid is thensuperimposed on the image. Units are classified by the apparent densityof people per unit. A cross, dot or other marking is placed on eachindividual's head or shadow point. Each marking is counted or, ifnecessary, estimated to determine the number of people in each gridunit. An error is then calculated based upon the number of grid unitsdivided by the degree of uncertainty about how many people each gridunit contains.

Aerial photographic analysis works under certain assumptions andconditions. Flying over the crowd during peak times requires an initialestimate of when that peak time occurs since photography is, bydefinition, a snapshot in time. The methods presented herein belowprovide continuous estimation for the duration of an event. Crowdestimation over time to find dynamic crowd size estimates may becritical for first responders and law enforcement to assess a volatilesituation before it reaches a flash point. For example, a rapidlygrowing mob could raise an alert to local law enforcement and eventorganizers.

Aerial photographic analysis also requires conditions that permit theuse of aircraft flying at a certain altitude. In contrast, the disclosedmethods allow a system to remotely analyze data collected from cellphones of the participants using wireless performance metrics. Analysisof the collected data can be done after the fact in situations where theevent was not advertised or promoted and aerial photography was notpermitted or planned.

Another pitfall of aerial photographic analysis occurs if the event isgeographically dispersed. In such situations, the cost of aerialanalysis increases along with the costs of coordinating the measurementat multiple event sites. Moreover, this method requires pre-knowledge ofthe event sites and does not permit after-the-fact analysis of crowdsizes. The disclosed method is agnostic of the number of locations orpre-knowledge since the disclosed data collection method can becontinuous. In some cases, enhanced metric collection may be needed andcan be activated remotely as needed. Thus, the disclosed methodaddresses a significant gap in cases where event sites aregeographically dispersed.

Crowd estimation using aerial photographic analysis requires some degreeof technical skill and, if done incorrectly (e.g., low resolution,blurry, or otherwise unusable photographs), will adversely impact thecrowd size estimates. In some cases, such as events at night or eventsheld in restricted spaces, aerial photography may not be possible. Thedisclosed methods can be used to complement aerial photography analysisin addition to addressing cases in which aerial photography methods isnot preferred or will not work due to low light levels, dispersedcrowds, vegetation, terrain, or participants being inside building ortemporary structures such as tents, port-a-potties, and the like.

The crowd size estimates provided by the disclosed method can reduce theerrors in aerial estimates by improving the degree of uncertainty abouthow many people attended a particular event. The disclosed methodprovides another dimension to event attendance.

Other methods for crowd estimation exist and have their own constraints.For example, crowd estimation by ground-level surveillance video, alsocalled detector-based analysis, is only accurate on a small scale. Thismethod requires extensive image analysis algorithms and is not scalableto large crowds. This method requires management of complex issues suchas motion detection, clustering, pixel change analysis, vanishingpoints, fractal analysis, perspective views, and video quality.

Another crowd estimation method uses measurements of dynamic flowsacross a monitored boundary. Several entry, exit, and intermediatepoints on a parade route are monitored and the rate of movement ofpeople across the boundary line is measured. These are extrapolated overtime to estimate a crowd size in attendance at the parade.

Yet another crowd estimation method uses crowd density and locationsegmentation. This method maps an event space into a series of segmentsand, based upon the dimensions of the segment, computes a packingdensity and, therefore, an attendance number for the event space. Thismethod has been used to estimate the crowd size at New Year's Eve eventsat Times Square in New York City, N.Y.

Another crowd estimation method uses the amount of trash produced byattendees. This method arose from the days when ticker tape paradesyielded measurable trash volumes. Trash production by modern era crowdsis comparatively small. Crowd estimation by trash volume is no longerviable due to many error factors. Similar modern methods that existmonitor port-a-potty use and extrapolate from there to determine crowdsize.

SUMMARY

The systems, methods, and computer program products described hereinprovide a unique way to leverage performance management (PM) databasesand systems to create data mining applications that use crowd sizeestimates. The present disclosure describes ways to determine andquantify a relationship between a number of cell phone users in a givenlocation and a total number of people at the location. The location maybe host to an event such as a sporting event, parade, festival, concert,marathon, holiday celebration, and the like.

As described herein, an analytical model is constructed withpopulation-specific parameters that take into consideration (1) wirelessnetwork performance data, (2) user data, and (3) transaction data toproduce a count of users at the location. Call detail records (CDR) andsignaling message analyses are used in implementation. Calibration andfeedback are used to refine and improve the analytical model toincorporate any additional factors that may impact crowd size. A marginof error associated with an estimate is also computed.

As also described herein, a statistical model is constructed todetermine the population parameters needed to solve the equationsprovided by the analytical model. Statistical analysis is used to modelreal-world applications to efficiently and reliably aid in understandingthe relationship between a general population and a subset of the samewith detectable cell phone service.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates a crowd size estimation architecture,according to an exemplary embodiment of the present disclosure.

FIG. 2 schematically illustrates a crowd size analyzer and componentsthereof, according to an exemplary embodiment of the present disclosure.

DETAILED DESCRIPTION

As required, detailed embodiments of the present disclosure aredisclosed herein. It must be understood that the disclosed embodimentsare merely exemplary examples of the disclosure that may be embodied invarious and alternative forms, and combinations thereof. As used herein,the word “exemplary” is used expansively to refer to embodiments thatserve as an illustration, specimen, model or pattern. The figures arenot necessarily to scale and some features may be exaggerated orminimized to show details of particular components. In other instances,well-known components, systems, materials or methods have not beendescribed in detail in order to avoid obscuring the present disclosure.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a basis for theclaims and as a representative basis for teaching one skilled in the artto variously employ the present disclosure.

Crowd Size Estimation Architecture

Referring now to the drawings in which like numerals represent likeelements throughout the several views, FIG. 1 schematically illustratesa crowd size estimation architecture 100, according to an exemplaryembodiment of the present disclosure. The crowd size estimationarchitecture 100 includes a location 102 in which a plurality ofwireless service users (“crowd”) 104 are located. Each user isassociated with a mobile device 106 through which the user communicateswith a wireless communications network 108 to carry out voice and/ordata communications with other users within or outside the location 102.

As used herein, the term “user” may refer to users that subscribe towireless service provided by an operator in accordance with a postpaidsubscription plan or users that pre-pay to the operator for wirelessservice. Thus, the crowd may consist of exclusively postpaid users,exclusively prepaid users, or a combination of postpaid users andprepaid users.

In the illustrated embodiment, the wireless communications network 108is a network provided by a single wireless service operator. As such,the wireless communications network 108 serves as the core or primarysource of crowd size estimation data. In some embodiments, however,other operators may license or host similar sources to provide theircrowd size estimation data based upon a proportional share of users toextrapolate to a total crowd size in the location 102.

In some embodiments, the wireless communications network 108 isconfigured to use exemplary telecommunications standards such as GlobalSystem for Mobile communications (GSM) and a Universal MobileTelecommunications System (UMTS). It should be understood, however, thatthe wireless communications network 108 may alternatively be configuredto use any existing or yet to be developed telecommunicationstechnology. Some examples of other suitable telecommunicationstechnologies include, but are not limited to, networks utilizing TimeDivision Multiple Access (TDMA), Frequency Division Multiple Access(FDMA), Wideband Code Division Multiple Access (WCDMA), OrthogonalFrequency Division Multiplexing (OFDM), Long Term Evolution (LTE), andvarious other 2G, 2.5G, 3G, 4G, and greater generation technologies.Examples of suitable data bearers include, but are not limited to,General Packet Radio Service (GPRS), Enhanced Data rates for GlobalEvolution (EDGE), the High-Speed Packet Access (HSPA) protocol family,such as, High-Speed Downlink Packet Access (HSDPA), Enhanced Uplink(EUL) or otherwise termed High-Speed Uplink Packet Access (HSUPA),Evolved HSPA (HSPA+), and various other current and future data bearers.

The illustrated wireless communications network 108 is in communicationwith a call detail record (CDR) database 110 and a signaling messagesdatabase 112. The CDR database 110 is a telecommunications performancemanagement (PM) component configured to store CDRs generated by thewireless communications network 108 (e.g., generated by a chargingsystem of the network 108). The signaling messages database 112 is atelecommunications control plane component configured to store signalingrecords from signaling operations of the wireless communications network108.

CDRs, as used herein, may refer to call detail records that includeinformation such as calling party number, called party number, date andtime of call initiation, date and time of call termination, duration ofcall, number charged for call, identifier of the network component thatgenerated the record, identifier of the record, result of the call(e.g., answered, busy, interrupted, and the like), call type (e.g.,voice call, messaging, data), and any faults that occurred during thecall.

The CDR database 110 and the signaling messages database 112 feed CDRsand signaling messages, respectively, into a crowd size analyzer 114. Inone embodiment, the crowd size analyzer 114 is configured as acombination of hardware and software. The crowd size analyzer 114includes an application configured to compute the crowd size based uponan analytical model of CDRs and signaling messages collected from thelocation 102 from a specified start time to a specified end time. Theparameters of the analytical model are produced through statisticalanalysis of the mobile device user base, as described in more detailherein. An exemplary crowd size analyzer 114 and components thereof areillustrated in FIG. 2, described herein below. The crowd size analyzer114 is also in communication with a customer database 116, a cell sitetopology and technology database 118, and a geographic informationsystem (GIS) map database 120.

The customer database 116 is configured to store customer data. Customerdata, in some embodiments, includes one or more of a mobile subscriberintegrated services digital network number (MSISDN), an internationalmobile subscriber identity (IMSI), an international mobile equipmentidentity (IMEI), a home cell identifier, a work cell identifier, a homenetwork identifier, a roaming network identifier, a most recentlocation, frequented locations, and the like. Customer data mayalternatively or additionally include demographics such as sex, race,age, income, disabilities, education, home ownership, employment status,home address, work address, and other demographics. In one embodiment,the customer database 116 is configured to provide all or a portion ofcustomer data to the crowd size analyzer 114 for statistical analysisusing the statistical model described herein.

The cell site topology and technology database 118 is configured tostore cell site information for at least a portion of the cell sites andassociated components in the wireless communications network 108. Insome embodiments, the cell site information includes information such asa location of at least some of the cell sites in the wirelesscommunications network 108, the network components at the cell sites(e.g., base transceiver stations, node-Bs, base station controllers, andthe like), the hardware version of the network components, the softwareversion of the network components, the firmware version of the networkcomponents, and the like. In one embodiment, the cell site topology andtechnology database 118 is configured to provide all or a portion of thecell site information to the crowd size analyzer 114 to model thepresence of one or more users at the location 102.

The GIS map database 120 is configured to store maps, such asortho-photo maps, and latitude/longitude coordinates mapped to streetaddresses or other locations. In one embodiment, the GIS map database120 is configured to provide the stored map, coordinate, and/or otherlocation data to the crowd size analyzer 114 to model the location 102.

The crowd size analyzer 114 is configured to generate one or more crowdsize reports 122, based on information received from the CDR database110 and the signaling messages database 112. A crowd size report 122 isused to share a computed crowd size with various entities. In oneembodiment, the crowd size analyzer 114 generates a crowd size report122 for sending to mobile devices 106 used by members of the crowd 104.In some embodiments, the crowd size report 122 is formatted as a shortmessaging service (SMS) message, an enhanced messaging service (EMS)message, a multimedia messaging service (MMS) service, an unstructuredsupplementary service data (USSD), an email, an instant message, a forummessage, a social networking message (e.g., Twitter® or Facebook®), andthe like. The crowd size report 122 includes the crowd size computed bythe crowd size analyzer 114 for the location 102. In some embodiments,the crowd size report 122 includes additional information such asdemographic information provided by the customer database 116.

In another embodiment, the crowd size analyzer 114 generates a report122 for delivery to a television 126 for audio and/or visualpresentation to a viewer. In some embodiments, the report 122 isprovided to the television 126 over-the-air or via a television serviceprovider such as a cable, satellite, or Internet protocol television(IPTV) provider. In some embodiments, the report 122 is presented asaudio, image, video, or some combination thereof. This information maybe presented on the television 126 alone, audibly or visuallysuperimposed, or in some other way to convey the crowd size informationincluded in the crowd size report 122 to those that have an interest inreceiving the crowd size information. The audio, image, or video datamay be formatted by the television service provider, a broadcastingcompany, or other entity based upon the needs of a particular hostingentity. Moreover, the codecs (e.g., audio and/or video codecs) used toencode and decode report data may be any selected by the hosting entity.

In another embodiment, the crowd size analyzer 114 generates a report122 for delivery to radio stations (not illustrated) for transmissionvia terrestrial radio towers, Internet, or satellite to those that havean interest in receiving the crowd size information.

In another embodiment, the crowd size analyzer 114 generates a crowdsize report 122 for delivery to a public safety answering point (PSAP)128. The PSAP 128 may deploy emergency services such as law enforcement,firefighters, and ambulance services to the location 102 in case of anemergency. Alternatively, the crowd size analyzer 114 generates a crowdsize report 122 for delivery directly to emergency services personnelsuch as law enforcement, firefighters, and ambulance services locatedproximate the location 102.

In another embodiment (not illustrated), the crowd size analyzer 114provides the report 122 to news outlets such as television news, newswebsites, and news web logs, message forums, social networkingapplications/websites, and the like.

Referring now to FIG. 2, the crowd size analyzer 114 and componentsthereof are illustrated, according to an embodiment of the presentdisclosure. Although connections are not shown between all componentsillustrated in FIG. 2, the components can interact with each other tocarry out various system functions described herein. It should beunderstood that FIG. 2 and the following description are intended toprovide a general description of a suitable environment in which thevarious aspects of some embodiments of the present disclosure can beimplemented.

The crowd size analyzer 114 includes a network interface 202 forfacilitating communications between the crowd size analyzer 114 andother systems 204 such as the CDR database 110, the signaling messagesdatabase 112, the customer database 116, the cell site topology andtechnology database 118, the GIS system map database 120, and othersystems or components of the wireless communications network 108. Thecrowd size analyzer 114 also includes one or more processors 206 thatare in communication with one or more memory modules 208 via one or morememory/data busses 210. The processor(s) 206 is configured to executeinstructions of a crowd size estimation analyzer application 212 storedon a tangible, non-transitory computer-readable medium, such as thememory module(s) 208, to facilitate computations of crowd size basedupon an analytical model using CDRs and signaling messages collectedfrom the location 102 from a specified start time to a specified endtime.

The term “memory,” as used herein to describe the memory module(s) 208,collectively includes all memory types associated with the crowd sizeanalyzer 114 such as, but not limited to, processor registers, processorcache, random access memory (RAM), other volatile memory forms, andnon-volatile, semi-permanent or permanent memory types; for example,tape-based media, tangible optical media, solid state media, hard disks,combinations thereof, and the like. While the memory module(s) 208 isillustrated as residing proximate the processor(s) 206, it should beunderstood that the memory module(s) 208 is in some embodiments aremotely accessible storage system. Moreover, the memory module(s) 208is intended to encompass network memory and/or other storage devices inwired or wireless communication with the crowd size analyzer 114. Thememory module(s) 208 may also store other data 214, which may includecached customer information, cell site information, map/locationinformation, signaling messages, CDRs, and the like.

Crowd Size Estimation Statistical Model Overview

A statistical model is used to aid in making crowd size estimationsbased upon information fed into the crowd size analyzer 114. Thestatistical model is used to determine the population parameters neededto solve the equations set forth by the analytical model, describedlater herein. Statistical variables used by the statistical model areillustrated below in Table A.

TABLE A Variable Definition U The overall population of users andnon-users. This may be a nationwide number or a specific sub-population,such as the population of single state or city. The statistical modelwill estimate the percentage of detectable mobile device users for thispopulation. U_(s) A sample population of users. W A discrete randomvariable used to count the number of users with mobile devices. Anoutput of market research. W_(d) A discrete random variable representingthe number of users with mobile devices that are that are detectable bythe wireless communications network. An output of the analytical modelin controlled settings. μ = E(W_(d)) = Σx × P(x) The expected value ofthe number of users with mobile devices that is detectable by thewireless communications network. σ = {square root over (V(W_(d)))} ={square root over (Σ(x − μ)² × P(x))}{square root over (Σ(x −μ)² × P(x))} The standard deviation of the number of users with mobiledevices that is detectable by the wireless communications network. C₀,C₁, C₂, . . . , C_(n) The wireless operators providing service at aparticular location. C₀ represents the population without wirelessservice. r₀, r₁, r₂, . . . , r_(n) Σ_(i=0) ^(n)r_(i) = 1 The marketshare percentage of wireless service provider C_(i) compared to thetotal population. r₀ is the proportion of people with no wirelessservice. These are outputs of market research and analysis in theregion/area/market in which the monitored location resides.$\alpha_{w} = \frac{µ}{U}$ The proportion of users with detectablewireless service in the overall population, U. N_(C) _(i) = |U_(s)| ×r_(i) × α_(w) The expected number of users with detectable wirelessservice from wireless operators C_(i) in the sample population, U_(s). EOverall error factor.

Calibrating the Statistical Model

The statistical model may need calibration from time to time forsuccessful estimation of crowd size. The statistical model uses samplingdistributions of a sample proportion of users with detectable mobiledevices in the overall population to correlate multiple random samplesof a specific size from a population and to provide populationparameters. Using the central limit theorem (CLT), it is determined thatthe sampling distributions will be approximately normally distributed.The overall proportion of users with detectable wireless service, α_(w),can be inferred with high confidence from the sample statistics.

In one embodiment, calibration is accomplished through sampling andanalysis at locations where near-exact crowd estimates are available. Inaddition or in the alternative, calibration is based upon industry dataand market research relating to operator coverage and wirelesssubscribers, per operator, segmented by geography.

Sampling in controlled environments such as football stadiums, amusementparks, and other event locations with turnstiles or other attendeecounting systems at an entry location can provide accurate crowd countsfor a known population that can be surveyed or sampled for wirelessusage. It can be determined based upon this information how many usershave wireless service, or, for each operator providing service, how manyusers in the sample receive service from a specific operator. Cell sitesof the wireless communications network 108 operating in the location 102are monitored and the analytical model (described below) is implementedby the crowd size analyzer 114 to determine the number of mobile devicesthat are detectable in the location 102. The model can be tuned orre-calibrated multiple times as needed to reduce estimation errors.

Margins of error are estimated by comparing the analysis at a footballstadium, for example, to market research on carrier coverage andsubscriber penetration. The estimation process has many potentialsources of error, each of which must be captured in the overall marginof error. Residual errors can be computed to show margins of errorsbetween controlled environments and environments analyzed by the crowdsize analyzer 114.

Crowd Size Estimation Analytical Model Overview

The analytical model includes several components that must be correctlycaptured to provide accurate crowd size estimation. These include anevents component, a locations component, an attendee component, asubscriber component, a baseline component, and an error component.

Multiple operators may provide wireless service at the location 102.Each operator provides wireless service to a portion or subset of thewireless service user base. The portion of wireless users associatedwith each operator is in some embodiments represented as a percentage.As such, in some embodiments, the analytical model considers wirelessservice users with service from all operators serving the location 102.In these embodiments, an instance of the crowd size analyzer 114 may beused by each operator. In one embodiment, each crowd size analyzerinstance feeds into a master crowd size analyzer (e.g., the illustratedcrowd size analyzer 114) located in a host network. The master crowdsize analyzer may be accessible by the operators via a data network suchas an intranet or the Internet. In one embodiment, each crowd sizeanalyzer instance feeds into a third party system that is external tothe operator networks.

If all operators serving the location 102 do not support a crowd sizeanalyzer instance, the analytical model is only able to count a subsetof mobile device users that use wireless service provided by aparticipating operator, This reduces the overall accuracy of crowd sizeestimations. That is, as the number of participating operatorsapproaches the total number of operators serving the location 102, theoverall accuracy of crowd size estimations increases. However, forreasons, financial or otherwise, operators may elect not to participatein crowd size estimation. As such, the analytical model is configured toproportionately estimate the remaining users using the statistical modeldescribed above, at the expense of a potentially higher margin of errorbased upon, for example, the availability and/or accuracy of marketresearch data of operator subscriber penetration in the location 102,among other factors. Variables used by the analytical model areillustrated below in Table B.

TABLE B Variable Definition s_(i,j), 1 < i, j < n The number of uniqueC_(j) subscribers detected at the location by wireless operator C_(i)using the disclosed method, i.e., output of the analytical model run bywireless operator C_(i) using CDRs and signaling messages from theoperator's network only. If i = j, then the subscribers are homesubscribers. Otherwise, the subscribers are roaming subscribers.$S_{i} = {\sum\limits_{j = 1}^{n}S_{ij}}$ The number of uniquesubscribers detected at the location for carrier C_(i) by all carriers.$N_{w} = {\sum\limits_{j = 1}^{n}S_{i}}$ The number of people inattendance with detectable mobile devices shown as the final output ofthe analytical model using CDRs and signaling messages.$ɛ_{i} = {r_{i} - \frac{S_{i}}{N_{w}}}$ The residual error where theanalytical and statistical models differ on the market share versusproportion of actual attendance at the location. If this is relativelylarge for the location (e.g., a specific event held at the location),the population is not representative of the statistical model.$\alpha_{w} = \frac{µ}{U}$ The proportion of people in attendance withdetectable mobile devices. An output of the statistical model tocalibrate the parameter used in the analytical model.$W = {\frac{N_{w}}{\alpha_{w}} \pm E}$ The number of people inattendance. This is the crowd size to be determined.

Profile of an Event Attendee and Wireless Service User

Generally, there is a high likelihood that through the duration ofmonitoring the location 102, an operational mobile device (i.e., amobile device that has sufficient remaining battery capacity to bepowered-on and attach to the wireless communications network 108) willbe recognized at some point by a radio access component or othercomponent of the wireless communications network 108. Although wirelessservice coverage in the location 102 may be intermittent or relativelypoor, unless service is unavailable, the mobile device will be detectedby the network 108.

When a wireless service user attaches to the wireless communicationsnetwork 108 via a mobile device, such as the illustrated mobile device106, the MSISDN, IMEI, and/or IMSI associated with the user isregistered in a performance metric collected by the wirelesscommunications network 108 in the location 102. The user's mobile devicemay be detected using network probes installed in one or more componentsof the wireless communications network 108 including, but not limitedto, radio access network components, packet core, and circuit corecomponents of the wireless communications network 108. In addition or inthe alternative, the user's mobile device may be detected using CDRanalysis, call setup signaling message analysis, call transfer signalingmessage analysis, call termination signaling message analysis, datasession initiation, short messaging service (SMS) messaging activity,other messaging activity (e.g., multimedia messaging service (MMS)),packet data protocol (PDP) context requests, and/or other control planeactivity. The most recent instance when a mobile device was detected atthe location 102 may also be recorded and used/not used based upon thetime frame during which the crowd size is monitored.

Modeling Events and Locations

An event may occur on a single date or a series of consecutive ornon-consecutive dates. Moreover, a event may occur duringnon-consecutive times, such as different time ranges on the same day(e.g., 9-11 AM and 3-5 PM on the same day), and one or more time rangeson one day and one or more time ranges on another day. An event mayoccur at a single location such as the illustrated location 102, or inmultiple, geographically dispersed locations. Each event location mayhave a start time and an end time for the event that defines theduration of the event. Activities scheduled to occur during the eventmay similarly include start and end times. The start and/or end timesmay include a time window, the times of which identify flexible,potential start times or end times. Additional time (e.g., seconds,minutes, hours, days, or other time increment) may be added to orsubtracted from the start and/or end times to advance, postpone orotherwise reschedule the event or one or more activities during an eventdue to inclement weather, natural disaster, terrorist attacks, fires,organizer preference, attendee preference, or other occurrence thatwarrants rescheduling of the event or event activities. In someembodiments, the crowd size analyzer 114 is configured to begin analysisat a time prior to the start of an event and/or end analysis at a timeafter the event.

In some embodiments, the system 100 is configured to allow assignment ofa criticality level to an event by an operator, news organization, eventorganizer, event attendee, government, business, individual, or thirdparty. In some embodiments, the criticality level of an event isassociated with a scale indicating various levels of criticality.Exemplary scales include written or verbal scales defined by terms suchas critical, major, minor, and like terminology, color scales whereindifferent colors represent different criticality levels, and numericscales wherein different numbers represent different criticality levels.Other scale types are contemplated. The criticality level aids the crowdsize analyzer 114 in determining a level of analysis that is appropriatefor a given event at a given location. The criticality level may bedirectly proportional to the accuracy of crowd size estimation needed ina particular situation. For example, certain events, such as apresidential inauguration, require high accuracy and thus could beassigned a critical or highest criticality level. In some embodiments,the crowd size analyzer 114 is configured to affect distribution ofcrowd size estimations, such as to whom and when (e.g., how often andhow close to real-time), and by which channels, based upon thecriticality level.

Each location in which an event can be held may include a list ofoperators that provide service in that location. Each operator has a mapof cell tower topology depicting service availability at that location.These maps include latitude/longitude coordinates for each cell site.These maps are stored in the cell site topology and technology database118 or similar database in each carrier's network or remote to eachcarrier for individual or collective access by the carriers. It iscontemplated that one or more operators may share a cell site topologyand technology database 118. The technology (e.g., GSM, UMTS, LTE, etc.)of each cell site, the frequency on which the cell tower communicates,and the sector geometry may also be stored in the database 118. Eachlocation may also have a baseline of presence associated therewith. Thisrepresents the number of people that, under normal operating conditions,are present at the location when no event is scheduled. Such personsinclude those that live in or within a predetermined proximity of theevent location, through traffic, persons arriving at the event locationbefore the event is scheduled to begin, and persons in the eventlocation during off-times (i.e., no event taking place). Events mayimpact the baseline of presence positively or negatively and adapt thebaseline over time. Determination and use of baselines is described inadditional detail below.

A map, such as an ortho-photo map of the terrain, is associated witheach location. These maps are stored in the GIS map database 120 which,similarly, may be included in each operator's network or remote to eachoperator for individual or collective access by the operators. It iscontemplated that one or more operators may share a map database 120.

Modeling Attendees and Baselines for Non-Event Related Activity at aLocation

Each wireless service user is associated with an MSISDN, an IMEI, and anIMSI by the operator serving that user. This information is stored inassociation with the user's name and other information such as physicaladdress, email address, alternate contact number (e.g., home telephonenumber), and/or other information, in the customer database 116. Eachuser can be identified by their MSISDN, IMEI, or IMSI.

Each user may be associated with an arrival time (i.e., the time atwhich a user's mobile device 106 is recognized within the location 102)and a departure time (i.e., the time at which a user's mobile device 106is no longer recognized within the location 102). Determination orrecording of the departure time may be time-buffered to allow mobiledevices 106 to reattach to the wireless communications network 108 inthe event signal is temporarily lost or otherwise interrupted. Forexample, if a mobile device 106 drops connection to a cell tower (e.g.,BTS, node-B) within the location 102, a departure time will not berecorded for the mobile device 106 through the duration of a time bufferto allow the mobile device 106 an opportunity to reattach.Alternatively, the departure time may be reset or adjusted if the samemobile device 106 reappears in the location 102 at a later time. Devices106 that leave and return may have multiple arrival and departure timesor be flagged as having such and adjusted to record the earliest/latestof the arrival time and the earliest/latest of the departure time basedupon the needs of a particular analysis.

The arrival time(s) and departure time(s) are used to determine aduration or time-spent-at-event. If an event occurs in multiplelocations, an arrival/departure time(s) and duration may be recorded foreach location the user attends.

In some embodiments, the crowd size analyzer 114 recognizes a definedrole for each person at the event. Each uniquely identified user istagged as, for example, an event visitor or an event non-visitor. Eventnon-visitors include persons that lives in proximity or otherwisetypically use their mobile devices to attach to a cell tower in theevent location, law enforcement, firefighters, emergency personnel(e.g., doctors, nurses, EMTs), media personnel, event organizers, andevent workers.

A person could be transiting through the event location by plane, boat,car, motorcycle, or other mode of transportation. In some embodiments,the model detects this by flagging short duration at the event locationor by cell tower handoff rates. Parameters may be defined for shortduration visits to account for through traffic.

Each person in the statistical model is associated with a wirelesscarrier even while roaming. The wireless carrier for each person may berecorded for analysis.

Calibration of the model often includes computing a baseline fornon-event related activity at the event location 102. Factors used forcomputing the baseline may include the number of people at the location102 on a normal, non-event day, a particular day of the week, the numberof workers at the location, the number of people that are non-eventrelated visitors, the number of people in transit through the location102, the number of actual event attendees at the location 102, and thecoverage distribution among all carriers serving the location 102. Aftera baseline is computed, variance above and below the baseline can bemeasure for each attendee type—e.g., event or non-event attendee.

Statistical and Analytical Modeling Errors

Environments such as stadiums may not be representative of an actualevent population due to poor signal within stadiums or other reasons.Accuracy of crowd estimation at these locations may be increased usingturnstile entry numbers obtained from stadium personnel coupled withavailable unique cellular activity data obtained from the CDR database110 and the signaling messages database 112.

The market share data and coverage data includes dynamic attributes thatchange often. Accordingly, the statistical model will be less accurateif it estimates parameters using historical market share and coveragedata. Error correction factors may be used to mitigate these effects.

The assumption that the number of unique people is proportionate to thenumber of cell phones present may be impacted by demographics of theattendees. For example, an event that attracts a younger age demographicmay trend to higher mobile device use and, thus, higher accuracy interms of actual attendees vs. attendees with cell phones. Otherdemographic trends associated with income, race, religion, and the likemay be indicative of a higher or lower propensity for mobile device use.

Detection errors may also be present in crowd size estimationcomputations. A false positive detection may occur when a user isidentified as an event attendee even though they would not characterizethemselves as such. A true negative may occur when a user did indeedattend an event but no PM metrics were collected from their mobiledevice 106, for example, due to their device 106 being in an inoperativestate such as by not being powered-on or having a faulty radiotransceiver. Error may also be attributed to attendees that simplycannot be detected. Examples include people without mobile devices 106,such as some children, people with mobile devices 106 in a power-offstate, and people with poor or no service in the event location.

Other detection errors may be attributed to attendees that have two ormore mobile devices 106 or if a single mobile device 106 has two or morerecords showing the same subscriber. Validation using the attendee'sMSISDN, IMSI, or IMEI against the customer database 116 records mayensure that the attendee is counted only once.

The statistical model can be improved using calibration techniquesdescribed above and through the participation of multiple wirelessoperators. As described above, if all wireless operators providingservice in the event location do not participate, additional error isintroduced in estimating crowd size at the event location. Errorcorrection factors based upon historical data and coverage penetrationmay be used to mitigate these effects.

Data Collection and Technical Issues

The disclosed systems and methods for computing crowd estimates assumethat an event occurs at a location with adequate wireless servicecoverage. Locations that experience poor or no wireless service coverageare not likely to yield valid crowd size estimation results whenanalyzed.

Gaps in performance metrics may exist. In some embodiments, this isresolved by provisioning special data collection configurations (e.g.,probes) on wireless communications network 108 components (e.g., radioaccess components, circuit core components, and packet core components)to collect performance data that would normally not be collected foranalysis. In other embodiments, data collected from these special datacollection configurations may be compared to data collected from the CDRdatabase 110 and/or the signaling messages database 112 for redundancyof collected data or comparison analysis. In still other embodiments,new systems are deployed to count the number of unique subscribersaccessing a particular cell site over a period of time. While thisapproach may be more accurate in certain locations that typicallyexperience relatively lower accuracy crowd size estimations, theadditional costs may be economically prohibitive for operators. The costof the new systems may be subsidized by those requesting crowd sizeestimations.

The accuracy and precision of a location definition may be decreased dueto inaccuracies in location determination techniques used to identifyand define the event location. This may lead to the crowd size analyzer114 falsely identifying non-attendees as attendees, and vice versa.

In some embodiments, the models are not be real-time models if CDRs andsignaling messages needed for analysis are not aggregated in performancemanagement business support systems. This may introduce a delay beforeanalysis can proceed.

RF interference due to overcrowding may also affect analysis results.The crowd could potentially overwhelm the ability of operators toservice all calls, resulting in dropped or missing data that introduceserror to the analysis. This can be prevented by appropriate networkengineering to support load at locations like the National Mall or otherlocation wherein events frequently occur.

The tool used herein (e.g., the wireless communications network 108) tomeasure an event metric (e.g., crowd size at a location within thewireless communications network 108 is affected by the event it ismonitoring. Thus, congestion and loss of performance data may lead toinaccuracies.

While the processes or methods described herein may, at times, bedescribed in a general context of computer-executable instructions, themethods, procedures, and processes of the present disclosure can also beimplemented in combination with other program modules and/or as acombination of hardware and software. The term application, or variantsthereof, is used expansively herein to include routines, programmodules, programs, components, data structures, algorithms, and thelike. Applications can be implemented on various system configurations,including servers, network systems, single-processor or multiprocessorsystems, minicomputers, mainframe computers, personal computers,hand-held computing devices, mobile devices, microprocessor-basedconsumer electronics, programmable electronics, network elements,gateways, network functions, devices, combinations thereof, and thelike.

The disclosed embodiments are merely examples that may be embodied invarious and alternative forms, and combinations thereof. As used herein,for example, “exemplary,” and similar terms, refer expansively toembodiments that serve as an illustration, specimen, model or pattern.The figures are not necessarily to scale and some features may beexaggerated or minimized, such as to show details of particularcomponents. In some instances, well-known components, systems, materialsor methods have not been described in detail in order to avoid obscuringthe systems, methods, and computer program products of the presentdisclosure. Therefore, specific structural and functional detailsdisclosed herein are not to be interpreted as limiting, but merely as abasis for the claims and as a representative basis for teaching oneskilled in the art.

The law does not require and it is economically prohibitive toillustrate and teach every possible embodiment of the present claims.Hence, the above-described embodiments are merely exemplaryillustrations of implementations set forth for a clear understanding ofthe principles of the disclosure. Variations, modifications, andcombinations may be made to the above-described embodiments withoutdeparting from the scope of the claims. All such variations,modifications, and combinations are included herein by the scope of thisdisclosure and the following claims.

What is claimed is:
 1. A method, comprising: determining, by aprocessor, a number of wireless service users at a location; estimating,by the processor, a total number of people at the location based uponthe number of wireless service users determined to be at the locationand based upon a statistical model; and calibrating the statisticalmodel based upon market research relating to operator coverage in thelocation and a known number of wireless service users associated witheach of a plurality of wireless service operators, at the location, bycomparing the estimated total number of people at the location with theknown number of wireless service users associated with each of theplurality of wireless service operators, at the location.
 2. The methodof claim 1, wherein determining the number of wireless service users atthe location comprises determining the number of wireless users at thelocation based upon call detail record information.
 3. The method ofclaim 1, wherein determining the number of wireless service users at thelocation is based upon signaling records associated with signalingoperations of a wireless communications network.
 4. The method of claim1, wherein determining the number of wireless service users at thelocation is further based upon a received subset of the number ofwireless service users at the location from a plurality of wirelessservice operators serving the location.
 5. The method of claim 1,wherein estimating the total number of people at the location based uponthe number of wireless service users determined to be at the location isfurther based upon an analytical model.
 6. The method of claim 1,wherein determining the number of wireless service users at the locationis further based upon network performance data.
 7. The method of claim6, wherein the network performance data is received from probes includedon wireless network components of a wireless communication network.
 8. Asystem, the system comprising: a processor; and a memory comprisinginstructions that, when executed by the processor, cause the processorto perform operations comprising: determining a number of wirelessservice users at a location; estimating a total number of people at thelocation based upon the number of wireless service users determined tobe at the location by executing a statistical model; and calibrating thestatistical model based upon market research relating to operatorcoverage in the location and a known number of wireless service usersassociated with each of a plurality of wireless service operators, atthe location by comparing the estimated total number of people at thelocation with the known number of wireless service users, associatedwith each of the plurality of wireless service operators, at thelocation.
 9. The system of claim 8, wherein the memory further comprisesinstructions that, when executed by the processor, cause the processorto perform determining the number of wireless service users at thelocation based upon call detail record information.
 10. The system ofclaim 8, wherein the memory further comprises instructions that, whenexecuted by the processor, cause the processor to perform determiningthe number of wireless service users at the location based uponsignaling records associated with signaling operations of a wirelesscommunications network.
 11. The system of claim 8, wherein the memoryfurther comprises instructions that, when executed by the processor,cause the processor to perform determining the number of wirelessservice users at the location based upon a received subset of the numberof wireless service users at the location from a plurality of wirelessservice operators serving the location.
 12. The system of claim 8,wherein the memory further comprises instructions that, when executed bythe processor, cause the processor to perform estimating the totalnumber of people at the location based upon the number of wirelessservice users determined to be at the location based upon an analyticalmodel.
 13. The system of claim 8, wherein the memory further comprisesinstructions that, when executed by the processor, cause the processorto perform determining the number of wireless service users at thelocation based upon network performance data.
 14. The system method ofclaim 13, wherein the network performance data is received from probesincluded on wireless network components of a wireless communicationnetwork.
 15. A non-transitory computer-readable storage mediumcomprising computer-executable instructions that, when executed by aprocessor, cause the processor to perform operations comprising:determining a number of wireless service users at a location; estimatinga total number of people at the location, based upon the number ofwireless service users determined to be at the location, by executing astatistical model; and calibrating the statistical model based uponmarket research relating to operator coverage in the location and aknown number of wireless service users associated with each of aplurality of wireless service operators, at the location by comparingthe estimated total number of people at the location with the knownnumber of wireless service users, associated with each of the pluralityof wireless service operators, at the location.
 16. The non-transitorycomputer-readable storage medium of claim 15, further comprisinginstructions that, when executed by the processor, cause the processorto perform determining the number of wireless service users at thelocation based upon call detail record information.
 17. Thenon-transitory computer-readable storage medium of claim 15, furthercomprising instructions that, when executed by the processor, cause theprocessor to perform determining the number of wireless service users atthe location based upon signaling records associated with signalingoperations of a wireless communications network.
 18. The non-transitorycomputer-readable storage medium of claim 15, further comprisinginstructions that, when executed by the processor, cause the processorto perform determining the number of wireless service users at thelocation based upon a received subset of the number of wireless serviceusers at the location from the plurality of wireless service operatorsserving the location.
 19. The non-transitory computer-readable storagemedium of claim 15, further comprising instructions that, when executedby the processor, cause the processor to perform estimating the totalnumber of people at the location based upon the number of wirelessservice users determined to be at the location based upon an analyticalmodel.
 20. The non-transitory computer-readable storage medium of claim15, further comprising instructions that, when executed by theprocessor, cause the processor to perform determining the number ofwireless service users at the location based upon network performancedata.